CN112418641A - Subway station safety evaluation method, device, server and storage medium - Google Patents

Subway station safety evaluation method, device, server and storage medium Download PDF

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CN112418641A
CN112418641A CN202011294614.7A CN202011294614A CN112418641A CN 112418641 A CN112418641 A CN 112418641A CN 202011294614 A CN202011294614 A CN 202011294614A CN 112418641 A CN112418641 A CN 112418641A
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李政道
甄宇
苏栋
刘炳胜
肖冰
谭颖恩
张丽梅
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Shenzhen University
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Abstract

The embodiment of the invention discloses a method, a device, a server and a storage medium for evaluating the safety of a subway station, wherein the method comprises the following steps: acquiring evaluation index data of a subway station; converting the evaluation index data through triangular fuzzy linguistic variables to construct a fuzzy judgment matrix; determining the maximum value of index weight data based on the fuzzy judgment matrix; determining a gray cluster evaluation matrix based on the index weight data maximum value; and performing safety assessment on the subway station based on the gray cluster evaluation matrix. According to the embodiment of the invention, the influence of the fuzzy evaluation index on the safety evaluation of the subway station is reduced, the defect that only the grey color of the index is considered in grey clustering is overcome, and the precision of the safety evaluation of the subway station is improved.

Description

Subway station safety evaluation method, device, server and storage medium
Technical Field
The embodiment of the invention relates to the technical field of rail transit, in particular to a method and a device for evaluating the safety of a subway station, a server and a storage medium.
Background
The subway is an important component in an urban traffic network, plays an important role in relieving urban traffic pressure, and provides strong driving force for urban development. Along with the continuous enlargement of subway line network scale, the subway passenger flow volume also increases day by day, which indicates that the requirement on the safety performance of subway stations is higher and higher.
At present, the safety assessment method for the subway station mainly comprises a probability risk assessment method, a fuzzy comprehensive assessment method, a Radial Basis Function (RBF) neural network analysis method, an entropy weight element method, a wireless sensor-digital-to-S (wireless sensor-digital-to-S) evidence theory method and the like. The probability risk evaluation method has high requirements on target data, and related data of subway stations are difficult to collect. The fuzzy comprehensive evaluation method carries out fuzzy evaluation on the indexes by using expert experience and knowledge, but ignores the problem of evaluation result distortion possibly caused by incomplete partial index information. The RBF neural network analysis method needs a certain amount of training, testing and learning samples, but the difficulty of sample collection is large due to the fact that the subway management level of each city is different. The WSR-D-S evidence theory method is complex in calculation, so that the evaluation efficiency is reduced.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, a server, and a storage medium for evaluating the safety of a subway station, so as to reduce the influence of a fuzzy evaluation index on the safety evaluation of the subway station and improve the precision of the safety evaluation of the subway station.
In a first aspect, an embodiment of the present invention provides a method for evaluating safety of a subway station, including:
acquiring evaluation index data of a subway station;
converting the evaluation index data through triangular fuzzy linguistic variables to construct a fuzzy judgment matrix;
determining the maximum value of index weight data based on the fuzzy judgment matrix;
determining a gray cluster evaluation matrix based on the index weight data maximum value;
and performing safety assessment on the subway station based on the gray cluster evaluation matrix.
Further, the transforming the evaluation index data through the triangular fuzzy linguistic variables to construct a fuzzy judgment matrix comprises:
comparing the evaluation index data pairwise according to the triangular fuzzy linguistic variables to obtain quantitative index data;
and constructing a fuzzy judgment matrix based on the quantization index data.
Further, after the two-by-two comparison of the evaluation index data is performed according to the triangular fuzzy linguistic variable to obtain quantitative index data, the method further includes:
and determining a membership function based on the quantitative index data.
Further, determining the maximum value of the index weight data based on the fuzzy judgment matrix comprises:
determining index weight data based on the fuzzy judgment matrix and the membership function;
and converting the index weight data into nonlinear programming weight data through fuzzy linear programming to determine the maximum value of the index weight data.
Further, before determining the gray cluster evaluation matrix based on the maximum index weight data, the method further includes:
constructing a gray class and a whitening weight function of a gray cluster of the subway station by using a central point triangular whitening weight function gray clustering method;
and constructing a clustering weight matrix based on the gray class and the whitening weight function.
Further, determining a gray cluster evaluation matrix based on the index weight data maximum value includes:
determining a primary index evaluation matrix based on the clustering weight matrix;
and determining a gray cluster evaluation matrix based on the index weight data maximum value and the primary index evaluation matrix.
Further, the safety assessment of the subway station based on the gray cluster evaluation matrix comprises:
determining a safety evaluation value of the subway station based on the gray clustering evaluation matrix and a preset threshold value;
and determining the safety level of the subway station based on the safety evaluation value of the subway station and a preset safety level classification table.
In a second aspect, an embodiment of the present invention provides a subway station safety evaluation device, including:
the system comprises an index data acquisition module, a data processing module and a data processing module, wherein the index data acquisition module is used for acquiring evaluation index data for evaluating the safety of a subway station;
the fuzzy judgment matrix construction module is used for converting the evaluation index data through triangular fuzzy linguistic variables to construct a fuzzy judgment matrix;
the weight maximum value determining module is used for determining the maximum value of index weight data based on the fuzzy judgment matrix;
the gray clustering module is used for determining a gray clustering evaluation matrix based on the maximum value of the index weight data;
and the safety evaluation module is used for carrying out safety evaluation on the subway station based on the gray clustering evaluation matrix.
In a third aspect, an embodiment of the present invention provides a server, including:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for evaluating the safety of the subway station provided by any embodiment of the invention.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for evaluating the safety of a subway station provided in any embodiment of the present invention.
The safety assessment method for the subway station, provided by the embodiment of the invention, comprises the steps of obtaining assessment index data of the subway station; converting the evaluation index data through triangular fuzzy linguistic variables to construct a fuzzy judgment matrix; determining the maximum value of index weight data based on the fuzzy judgment matrix; determining a gray cluster evaluation matrix based on the index weight data maximum value; performing safety assessment on the subway station based on the gray clustering evaluation matrix; the method reduces the influence of fuzzy evaluation indexes on the safety evaluation of the subway, overcomes the defect that only the grey color of the indexes is considered in grey clustering, and improves the precision of the safety evaluation of the subway station.
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Fig. 1 is a schematic flow chart of a method for evaluating safety of a subway station according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for evaluating safety of a subway station according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a subway station safety evaluation device according to a third embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a server according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. The terms "first", "second", etc. are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "plurality", "batch" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Example one
Fig. 1 is a schematic flow chart of a method for evaluating safety of a subway station according to an embodiment of the present invention, which is applicable to safety evaluation of a subway station. As shown in fig. 1, a method for evaluating safety of a subway station according to an embodiment of the present invention includes:
and S110, obtaining evaluation index data of the subway station.
Specifically, the evaluation indexes refer to items that affect the safety performance of the subway station, and in this embodiment, the evaluation indexes of the subway station are classified, including 6 primary indexes and 20 secondary indexes, as shown in table 1. In this embodiment, the evaluation index data is data of each secondary index of the subway station, and most of the evaluation index data is qualitative data.
Table 1 evaluation index data detail table
Figure BDA0002784978660000061
And S120, converting the evaluation index data through the triangular fuzzy linguistic variables to construct a fuzzy judgment matrix.
Specifically, the evaluation index data is data having a certain relevance to the safety performance of the subway station, is qualitative data, and generally has a certain ambiguity. The triangular fuzzy linguistic variable is a method for quantizing fuzzy data, and can quantize qualitative data to convert the qualitative data into quantized data, namely, the triangular fuzzy linguistic variable defines the incidence relation between the qualitative data and the quantized data. And (3) converting the triangular fuzzy linguistic variables of the evaluation index data, namely comparing every two evaluation index data by experts to determine a qualitative relation between the two evaluation index data, and determining corresponding quantitative data according to the triangular fuzzy linguistic variables to obtain the quantitative index data. The fuzzy judgment matrix is a matrix formed by the converted quantization index data, one data in the fuzzy judgment matrix is called an index element, and one index element in the fuzzy judgment matrix is a converted triangular fuzzy number.
And S130, determining the maximum value of the index weight data based on the fuzzy judgment matrix.
Specifically, the index weight data refers to a weight value of each row of index elements in the fuzzy judgment matrix. And determining the maximum value of the index weight data based on the fuzzy judgment matrix, wherein the method mainly comprises the steps of determining the index weight data in the fuzzy judgment matrix and then determining the maximum value of the index weight data through nonlinear programming.
And S140, determining a gray cluster evaluation matrix based on the maximum index weight data.
Specifically, gray clustering is a method of aggregating some observation indicators or observation objects into several definable categories according to a gray correlation matrix or a whitening weight function of gray number. Determining a gray clustering evaluation matrix based on the maximum value of the index weight data, firstly constructing a primary evaluation matrix through the maximum value of the index weight data, then constructing a clustering weight matrix according to gray classes and whitening weight functions of the subway station, and finally forming the gray clustering evaluation matrix based on the product of the clustering weight matrix and the primary evaluation matrix.
S150, carrying out safety assessment on the subway station based on the gray cluster evaluation matrix.
Specifically, safety evaluation is performed on the subway station based on the gray clustering evaluation matrix, namely, a safety measurement value of the subway station is obtained according to the gray clustering evaluation matrix, and then the safety level of the subway station is determined based on the safety level corresponding to the safety measurement value in the safety level division table.
The method for evaluating the safety of the subway station, provided by the embodiment of the invention, comprises the steps of obtaining evaluation index data of the subway station; converting the evaluation index data through triangular fuzzy linguistic variables to construct a fuzzy judgment matrix; determining the maximum value of index weight data based on the fuzzy judgment matrix; determining a gray cluster evaluation matrix based on the index weight data maximum value; performing safety assessment on the subway station based on the gray clustering evaluation matrix; the method reduces the influence of fuzzy evaluation indexes on the safety evaluation of the subway, overcomes the defect that only the grey color of the indexes is considered in grey clustering, and improves the precision of the safety evaluation of the subway station.
Example two
Fig. 2 is a schematic flow chart of a method for evaluating safety of a subway station according to a second embodiment of the present invention, which is a further refinement of the above embodiments. As shown in fig. 2, a method for evaluating the safety of a subway station according to a second embodiment of the present invention includes:
s201, obtaining evaluation index data of the subway station.
Specifically, the evaluation index data is referred to table 1.
S202, comparing the evaluation index data pairwise according to the triangular fuzzy linguistic variables to obtain quantitative index data.
In this embodiment, the quantized index data is obtained by quantizing the index data by an expert according to the triangular fuzzy linguistic variable. The triangular fuzzy linguistic variables are shown in table 2. According to table 2, the evaluation index data are compared pairwise to determine the qualitative relationship between the evaluation index data and the evaluation index data (i.e., the linguistic variables in table 2), and then converted into corresponding quantitative index data (i.e., the triangular fuzzy numbers in table 2).
TABLE 2 triangular fuzzy linguistic variables relational table
Linguistic variables Triangular fuzzy number Linguistic variables Triangular fuzzy number
Of equal importance (1，1，1) Median of the two (5，6，7)
Median of the two (1，2，3) Is very important (6，7，8)
Of greater importance (2，3，4) Median of the two (7，8，9)
Median of the two (3，4，5) Of absolute importance (9，9，9)
Of importance (4，5，6)
In this embodiment, the number of triangular ambiguities is denoted as M ≦ u, l denotes the lower bound of the number of triangular ambiguities, M denotes the middle bound of the number of triangular ambiguities, u denotes the upper bound of the number of triangular ambiguities, u-l denotes the height of the ambiguity, and when u ═ M ≦ l, M is the non-ambiguity determination.
S203, constructing a fuzzy judgment matrix based on the quantization index data.
Specifically, the known fuzzy prioritization method needs a complete set of m ═ n (n-1)/2 comparison judgments, so that pairwise comparisons are performed on the secondary indexes in table 1 according to the triangular fuzzy linguistic variables in table 2, a positive reciprocal matrix for pairwise comparisons is constructed, and a fuzzy judgment matrix a ═ is obtainedij]As follows:
Figure BDA0002784978660000091
wherein n is the number of the second-level indexes under a certain one-level index in the table 1, and i and j respectively represent the row and the column of the fuzzy judgment matrix A; a isij＝(lij,mij,nij)，aji＝1/aij＝(1/lij,1/mij,1/nij)。
And S204, determining a membership function based on the quantization index data.
In this embodiment, the membership function is denoted as uM(x) The representation is as follows:
Figure BDA0002784978660000092
s205, determining index weight data based on the fuzzy judgment matrix and the membership function.
Firstly, determining a weight value of an index element in the ith row in a fuzzy judgment matrix A, which specifically comprises the following steps: determining the product M of each row element of the fuzzy judgment matrix AiI.e. by
Figure BDA0002784978660000093
According to the product M of each row elementiObtain the relative importance Wi 0，
Figure BDA0002784978660000094
To relative importance Wi 0Carrying out normalization processing to obtain the weight value w of the index element of the ith rowi，
Figure BDA0002784978660000095
The index weight data is:
Figure BDA0002784978660000096
wherein l and u are the minimum and maximum values of the evaluation, and m is a value between the minimum and maximum values; w is aiJudging the weight value of the index element in the ith row in the matrix A for the fuzzy judgment; w is ajAnd judging the weight value of the j-th line index element in the matrix A for the fuzzy judgment.
When in use
Figure BDA0002784978660000101
Or
Figure BDA0002784978660000102
When the temperature of the water is higher than the set temperature,
Figure BDA0002784978660000103
this indicates that the blur determination matrix has poor consistency.
When in use
Figure BDA0002784978660000104
When the temperature of the water is higher than the set temperature,
Figure BDA0002784978660000105
taking the maximum value 1, defining the minimum value of the membership degree (namely index weight data) of the weight vector ratio: u. ofp(w)＝min{uij(w)|i＝1,2,…n-1；j＝2,3,…,n；j＞i}；
The largest vector is selected as the solution vector: λ ═ up(w*)＝max{up(w)}。
S206, converting the index weight data into nonlinear programming weight data through fuzzy linear programming to determine the maximum value of the index weight data.
Specifically, the steps S202 to S205 are collectively referred to as a Fuzzy Preference Programming (FPP), that is, the index weight data is calculated by the FPP according to the embodiment of the present invention. Converting the index weight data into nonlinear programming weight data through fuzzy linear programming, namely converting the FPP into the nonlinear programming through the fuzzy linear programming, which is concretely as follows:
maxλ
Figure BDA0002784978660000106
after nonlinear conversion, the optimal solution (w) is quickly obtained by means of Matlab software*,λ*)。
w*Is the maximum weight (index weight data maximum) of the membership degree, wkAs an index weight, λ*The evaluation value of the consistency of the matrix is judged for the fuzzy.
When lambda is larger than 0, the fuzzy judgment matrix is good in consistency.
And when the lambda is less than 0, the consistency of the fuzzy judgment matrix is poor, the evaluation index data is compared again to obtain a new fuzzy judgment matrix, namely, the step S202 is returned until the lambda is more than 0.
S207, constructing a gray class and a whitening weight function of the gray cluster of the subway station by using a central point triangular whitening weight function gray clustering method.
Specifically, the gray class and whitening weight function of the gray cluster of the subway station can be constructed by referring to table 3.
TABLE 3 Grey class and whitening weight function relationship Table
Figure BDA0002784978660000111
And S208, constructing a clustering weight matrix based on the gray class and the whitening weight function.
Specifically, first, an evaluation matrix D needs to be establishedi. Issuing questionnaires to p experts in questionnaire formEach secondary index in the table 1 is subjected to scoring assignment, and an evaluation matrix is established
Figure BDA0002784978660000112
Wherein d isijkThe expert assigns (k is 1, 2, …, p) to the secondary index j under the index i, and s is the number of evaluation factors.
Then determining the clustering weight coefficient X of each gray classijeAnd the total clustering weight coefficient XijAs follows:
Figure BDA0002784978660000121
the clustering coefficient of the grey e under a secondary index is obtained;
Figure BDA0002784978660000122
is the total clustering coefficient.
According to the clustering weight coefficient X of each gray classijeAnd the total clustering weight coefficient XijObtaining a clustering weight vector
Figure BDA0002784978660000123
The cluster weight vector is an element in the cluster weight matrix.
Obtaining a clustering weight matrix R according to the clustering weight vectoriAs follows:
Figure BDA0002784978660000124
s209, determining a primary index evaluation matrix based on the clustering weight matrix.
Specifically, a primary index evaluation matrix Z is constructed based on the maximum value of index weight data and the clustering weight matrixi，Zi＝wi·Ri。
S210, determining a gray cluster evaluation matrix based on the index weight data maximum value and the primary index evaluation matrix.
Specifically, a primary index score is obtained based on the primary index evaluation matrixThe value matrix Z, Z ═ Z1,Z2,…,Zn]。
Maximum value w of scalar weight data*The product of the first-level index evaluation matrix Z is a gray clustering evaluation matrix M, and M is w*·Z＝[M1,M2,…,Mn]。
S211, determining a safety evaluation value of the subway station based on the gray clustering evaluation matrix and a preset threshold value.
Specifically, combining the range of the safety level of the subway station, taking a central point vector as a preset threshold, wherein U is (9, 7, 5, 3, 1), multiplying the gray clustering evaluation matrix M by the preset threshold U, reducing the secondary loss of data information, and obtaining the safety evaluation value W of the subway station*，W*＝M·UT。
S212, determining the safety level of the subway station based on the safety evaluation value of the subway station and a preset safety level classification table.
In this embodiment, the safety level of the subway station is divided into 5 levels, as shown in table 4. According to the safety assessment value W of the subway station*The corresponding security level of the subway station can be determined with reference to table 4.
TABLE 4 subway station safety class demarcation table
Level of security Specification of index grade Measure (a)
Ⅰ Is very safe [8，10)
Ⅱ Secure [6，8)
Ⅲ Warning [4，6)
Ⅳ Danger of [2，4)
Ⅴ Extreme danger (0，2)
When the safety level is level I, the station safety situation is in an ideal state, passengers can safely, quickly and comfortably go out, and all systems of the station can well run; when the safety level is level II, negligible or delay processing equipment faults and passenger violation events influencing the station safety situation exist, the station passenger flow volume increasing equipment operates normally, and passengers travel safely; when the safety level is level III, equipment failure and slight passenger injury events occur, station management needs to be enhanced, and prevention and control measures are taken to ensure the trip safety of passengers and the station operation safety; when the safety level is IV level, the station safety situation becomes worse, dangerous accidents and passenger accidents occur to influence the normal operation of the station; when the safety level is V level, the station safety situation is in a dangerous state, serious equipment faults and serious passenger injury accidents occur, and the personal and property safety of passengers and the normal operation of the station are seriously endangered.
According to the subway station safety assessment method provided by the embodiment of the invention, the weight of the index is obtained by constructing the fuzzy priority plan, so that the influence of the fuzzy evaluation index on the subway safety assessment is reduced, the complexity of consistency inspection is overcome, and the scientificity of empowerment is improved; the negative effect of extreme values caused by a pure scoring method of experts is weakened, the defect that only index grey color is considered in grey clustering is overcome, and the precision of safety assessment of the subway station is improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a safety evaluation device for a subway station according to a third embodiment of the present invention, which is applicable to safety evaluation of a subway station in this embodiment. The safety assessment device for the subway station provided by the embodiment can realize the safety assessment method for the subway station provided by any embodiment of the invention, has corresponding functional structures and beneficial effects of the realization method, and the content which is not described in detail in the embodiment can refer to the description of any method embodiment of the invention.
As shown in fig. 3, a subway station safety evaluation device provided by a third embodiment of the present invention includes: an index data obtaining module 310, a fuzzy judgment matrix constructing module 320, a weight maximum value determining module 330, a gray clustering module 340 and a security evaluating module 350, wherein:
the index data obtaining module 310 is configured to obtain evaluation index data for evaluating safety of a subway station;
the fuzzy judgment matrix construction module 320 is used for transforming the evaluation index data through triangular fuzzy linguistic variables to construct a fuzzy judgment matrix;
the weight maximum value determining module 330 is configured to determine a maximum value of the index weight data based on the fuzzy judgment matrix;
the gray clustering module 340 is configured to determine a gray clustering evaluation matrix based on the maximum index weight data;
the safety evaluation module 350 is configured to perform safety evaluation on the subway station based on the gray cluster evaluation matrix.
Further, the fuzzy judgment matrix building module 320 includes:
the quantization unit is used for comparing the evaluation index data pairwise according to the triangular fuzzy linguistic variables to obtain quantization index data;
and the fuzzy judgment matrix construction unit is used for constructing a fuzzy judgment matrix based on the quantization index data.
Further, the method also comprises the following steps:
and the membership function determining module is used for determining a membership function based on the quantitative index data.
Further, the weight maximum determination module 330 includes:
the index weight data determining unit is used for determining index weight data based on the fuzzy judgment matrix and the membership function;
and the nonlinear conversion unit is used for converting the index weight data into nonlinear programming weight data through fuzzy linear programming so as to determine the maximum value of the index weight data.
Further, the method also comprises the following steps:
the whitening weight function determining module is used for constructing the gray class and whitening weight function of the gray cluster of the subway station by using a central point triangular whitening weight function gray clustering method;
and the clustering weight matrix building module is used for building a clustering weight matrix based on the gray class and the whitening weight function.
Further, the gray clustering module 340 includes:
a primary index evaluation matrix determination unit, configured to determine a primary index evaluation matrix based on the clustering weight matrix;
and the gray cluster evaluation matrix determining unit is used for determining a gray cluster evaluation matrix based on the index weight data maximum value and the primary index evaluation matrix.
Further, the security assessment module 350 includes:
the safety evaluation value determining unit is used for determining a safety evaluation value of the subway station based on the gray clustering evaluation matrix and a preset threshold value;
and the safety level determining unit is used for determining the safety level of the subway station based on the safety evaluation value of the subway station and a preset safety level classification table.
According to the subway station safety evaluation device provided by the third embodiment of the invention, through the index data acquisition module, the fuzzy judgment matrix construction module, the weight maximum value determination module, the gray clustering module and the safety evaluation module, the influence of fuzzy evaluation indexes on subway safety evaluation is reduced, the defect that only the gray color of the indexes is considered in gray clustering is overcome, and the precision of subway station safety evaluation is improved.
Example four
Fig. 4 is a schematic structural diagram of a server according to a fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary server 412 suitable for use in implementing embodiments of the present invention. The server 412 shown in fig. 4 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention.
As shown in FIG. 4, server 412 is in the form of a general purpose server. Components of server 412 may include, but are not limited to: one or more processors 416, a storage device 428, and a bus 418 that couples the various system components including the storage device 428 and the processors 416.
Bus 418 represents one or more of any of several types of bus structures, including a memory device bus or memory device controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Server 412 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by server 412 and includes both volatile and nonvolatile media, removable and non-removable media.
Storage 428 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 430 and/or cache Memory 432. The server 412 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 434 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk such as a Compact disk Read-Only Memory (CD-ROM), Digital Video disk Read-Only Memory (DVD-ROM) or other optical media may be provided. In these cases, each drive may be connected to bus 418 by one or more data media interfaces. Storage 428 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 440 having a set (at least one) of program modules 442 may be stored, for instance, in storage 428, such program modules 442 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. The program modules 442 generally perform the functions and/or methodologies of the described embodiments of the invention.
The server 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing terminal, display 424, etc.), with one or more terminals that enable a user to interact with the server 412, and/or with any terminals (e.g., network card, modem, etc.) that enable the server 412 to communicate with one or more other computing terminals. Such communication may occur via input/output (I/O) interfaces 422. Further, server 412 may communicate with one or more networks (e.g., a Local Area Network (LAN), Wide Area Network (WAN), and/or a public Network, such as the Internet) via Network adapter 420. As shown in FIG. 4, network adapter 420 communicates with the other modules of server 412 via bus 418. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the server 412, including but not limited to: microcode, end drives, Redundant processors, external disk drive Arrays, RAID (Redundant Arrays of Independent Disks) systems, tape drives, and data backup storage systems, among others.
The processor 416 executes various functional applications and data processing by running programs stored in the storage device 428, for example, implementing a method for evaluating the safety of a subway station provided by any embodiment of the present invention, which may include:
acquiring evaluation index data of a subway station;
converting the evaluation index data through triangular fuzzy linguistic variables to construct a fuzzy judgment matrix;
determining the maximum value of index weight data based on the fuzzy judgment matrix;
determining a gray cluster evaluation matrix based on the index weight data maximum value;
and performing safety assessment on the subway station based on the gray cluster evaluation matrix.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for evaluating safety of a subway station, where the method includes:
acquiring evaluation index data of a subway station;
converting the evaluation index data through triangular fuzzy linguistic variables to construct a fuzzy judgment matrix;
determining the maximum value of index weight data based on the fuzzy judgment matrix;
determining a gray cluster evaluation matrix based on the index weight data maximum value;
and performing safety assessment on the subway station based on the gray cluster evaluation matrix.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A safety assessment method for a subway station is characterized by comprising the following steps:
acquiring evaluation index data of a subway station;
converting the evaluation index data through triangular fuzzy linguistic variables to construct a fuzzy judgment matrix;
determining the maximum value of index weight data based on the fuzzy judgment matrix;
determining a gray cluster evaluation matrix based on the index weight data maximum value;
and performing safety assessment on the subway station based on the gray cluster evaluation matrix.
2. The method of claim 1, wherein transforming the evaluation index data by triangulating fuzzy linguistic variables, and constructing a fuzzy judgment matrix comprises:
comparing the evaluation index data pairwise according to the triangular fuzzy linguistic variables to obtain quantitative index data;
and constructing a fuzzy judgment matrix based on the quantization index data.
3. The method of claim 2, wherein after comparing the evaluation index data pairwise according to the triangular fuzzy linguistic variables to obtain the quantitative index data, further comprising:
and determining a membership function based on the quantitative index data.
4. The method of claim 3, wherein determining an index weight data maximum value based on the fuzzy decision matrix comprises:
determining index weight data based on the fuzzy judgment matrix and the membership function;
and converting the index weight data into nonlinear programming weight data through fuzzy linear programming to determine the maximum value of the index weight data.
5. The method of claim 1, wherein prior to determining a gray cluster evaluation matrix based on the index weight data maximum, further comprising:
constructing a gray class and a whitening weight function of a gray cluster of the subway station by using a central point triangular whitening weight function gray clustering method;
and constructing a clustering weight matrix based on the gray class and the whitening weight function.
6. The method of claim 5, wherein determining a gray cluster evaluation matrix based on the metric weight data maximum comprises:
determining a primary index evaluation matrix based on the clustering weight matrix;
and determining a gray cluster evaluation matrix based on the index weight data maximum value and the primary index evaluation matrix.
7. The method of claim 1, wherein performing a security assessment of the subway station based on the gray cluster evaluation matrix comprises:
determining a safety evaluation value of the subway station based on the gray clustering evaluation matrix and a preset threshold value;
and determining the safety level of the subway station based on the safety evaluation value of the subway station and a preset safety level classification table.
8. A subway station safety evaluation device, characterized by comprising:
the system comprises an index data acquisition module, a data processing module and a data processing module, wherein the index data acquisition module is used for acquiring evaluation index data for evaluating the safety of a subway station;
the fuzzy judgment matrix construction module is used for converting the evaluation index data through triangular fuzzy linguistic variables to construct a fuzzy judgment matrix;
the weight maximum value determining module is used for determining the maximum value of index weight data based on the fuzzy judgment matrix;
the gray clustering module is used for determining a gray clustering evaluation matrix based on the maximum value of the index weight data;
and the safety evaluation module is used for carrying out safety evaluation on the subway station based on the gray clustering evaluation matrix.
9. A server, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for assessing the safety of a subway station as claimed in any one of claims 1 to 7.
CN202011294614.7A 2020-11-18 2020-11-18 Subway station safety evaluation method, device, server and storage medium Pending CN112418641A (en)

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